The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also be inventions.
Osteoporosis is a major public health threat for over 50% of the U.S. population over age 50—an estimated 44 million American women and men—and with the growing size of the elderly population, the need for improved diagnosis and monitoring of drug treatments continues to increase. About 10 million Americans are estimated to already have the disease, the other 34 million with low bone mass (or “osteopenia”) are at increased risk for osteoporosis. The most serious complication of osteoporosis is a fractured bone, particularly at the hip. Other types of osteoporotic fractures include spine fractures, the most common type of osteoporotic fracture, and also fractures of the wrist, humerus, ribs, and other smaller bones. One problem in clinically managing osteoporosis is that too few women and men are identified with this disease, and therefore too few are placed on the widely available therapeutic treatments that are known to reduce fracture incidence. As a result of this under-testing, there is a need for additional types of clinical tests that can be implemented in a consistent and widespread manner and that are effective at identifying patients at high risk of an osteoporotic fracture.
The current clinical standard test for identifying people at high risk of osteoporotic fracture is the dual energy X-ray absorptiometry test (more often referred to as DEXA or DXA). The main outcome of the DXA test is a measure of bone mineral density (BMD, in units of g/cm2). To facilitate comparison of BMD values for different types of DXA machines, the BMD value from a DXA test is usually reported as a “T-score”, defined as how much the patient's BMD is below the average BMD of a young reference population, expressed in terms of standard deviations of BMD for that reference population. So, for example, if a patient has a T-score of −1.2, it indicates that the patient's BMD is 1.2 standard deviations below the average BMD in the young reference population. The most widely adopted clinical definition of osteoporosis is a BMD T-score of −2.5 or lower, with another condition, known as “osteopenia” or “low bone mass”, defined as when the BMD T-score is between −2.5 and −1.0; “normal” BMD is when the T-score is −1.0 or higher. The BMD can be measured at the hip or spine, and usually if the T-score from any of these sites is −2.5 or lower, the patient is said to have osteoporosis; if all T-scores are greater than −2.5 but at least one T-score is between −2.5 and −1.0, then the patient is said to have osteopenia. Another established clinical test is quantitative computed tomography (CT), which provides a measure of BMD (in units of mg/cm3) of the vertebral trabecular bone. When this measure of BMD is less than 80 mg/cm3, a patient is considered to have osteoporosis. See for example, Engelke (Engelke K, Adams J E, Armbrecht G, Augat P, Bogado C E, Bouxsein M L, Felsenberg D, Ito M, Prevrhal S, Hans D B, Lewiecki E M. Clinical use of quantitative computed tomography and peripheral quantitative computed tomography in the management of osteoporosis in adults: the 2007 ISCD Official Positions. J Clin Densitom. 11:123-62, 2008). Quantitative CT of the hip can also be used to provide a DXA-equivalent BMD at the hip, and the associated T-scores; see for example, Khoo (Khoo (Comparison of QCT-derived and DXA-derived areal bone mineral density and T scores. Khoo B C, Brown K, Cann C, Zhu K, Henzell S, Low V, Gustafsson S, Price R I, Prince R L. Osteoporos Int. 20:1539-45, 2009).
Many fracture-outcome clinical studies have shown that patients who have osteoporosis are indeed at high risk of fracture, and as a result of such findings, a patient with BMD-defined osteoporosis is a good candidate for therapeutic treatment. Indeed, the BMD values that correspond to a definition of osteoporosis have become an almost universally accepted interventional threshold value for the treatment of elderly patients with osteoporosis. That is to say, patients with a BMD score equal to or less than the interventional threshold value for osteoporosis are recommended for some form of therapeutic intervention or treatment. In general, without a well validated and clinically accepted interventional threshold value, the results of any new bone test would be difficult to implement clinically since physicians would otherwise not know how to consistently interpret the results of such a new test in terms of deciding whether or not to recommend that a patient undergo therapeutic treatment.
A second challenge for any new bone tests relates to the clinical finding that about half of osteoporotic hip fractures occur in elderly people who do not have osteoporosis, but who instead only have osteopenia. Thus, there remains a need for new clinical tests that can identify high-risk patients with osteoporosis and also some high-risk patients with osteopenia who are nonetheless at high risk of fracture.
One such potential alternative test to DXA is based on the method of finite element analysis of computed tomography (CT) scans for patient-specific assessment of bone strength, termed here “Biomechanical CT” analysis, or “BCT”. See, for example, U.S. Pat. No. 5,172,695 for a description of such finite element analysis of quantitative CT scans for clinical bone strength analysis, which is expressly included herein in its entirety by reference, for all purposes. See also Faulkner et al. (Effect of bone distribution on vertebral strength: assessment with patient-specific nonlinear finite element analysis. Radiology. 179:669-674, 1991), Keyak et al. (Prediction of femoral fracture load using automated finite element modeling. J Biomech. 31:125-133, 1998), Cody et al. (Femoral strength is better predicted by finite element models than QCT and DXA. J Biomech. 32:1013-1020, 1999), Keyak (Relationships between femoral fracture loads for two load configurations. J Biomech. 33:499-502, 2000), Crawford et al. (Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography. Bone. 33:744-750, 2003), and Nicolella and Bredbenner (Development of a parametric finite element model of the proximal femur using statistical shape and density modelling. Comput Methods Biomech Biomed Engin. 15:101-10, 2012)—all of which are expressly included herein in their entireties by reference.
In development in academia for over 20 years, the BCT method combines image processing of clinical CT scans, bone biomechanics, and the engineering computational mechanics technique of finite element analysis to provide a “virtual stress test” of a bone. The primary outcome of the test is an estimate of the strength (in units of Newtons) of the whole bone or a portion thereof, for example, a femur, a vertebral body, or a proximal femur. Cadaver studies have shown that the BCT method provides excellent non-invasive estimates of bone strength for the hip and spine and other bones. Clinical fracture-outcome studies have also shown that the estimates of bone strength obtained from the BCT technique are significantly associated with the risk of fracture. Despite its potential and its long history, the BCT technique has remained a niche research tool and has not yet been developed as an effective clinical test that can reliably identify patients at high risk of fracture in clinical practice. Part of the reason for this is the difficulty in establishing a credible and robust interventional threshold value for the BCT-generated values of bone strength that can be confidently adopted by clinicians, and approved by federal regulators.
This same challenge applies to the clinical use of results from any type of bone structural analysis performed on any type of medical image. For example, two-dimensional dual energy X-ray (DXA) scans of a bone can be processed to provide structural measures associated with the strength and structure of the bone, including but not limited to a buckling ratio, a femoral neck diameter, a hip axis length, various different composite bending and compressive strength indices, as well as measures of trabecular microarchitecture, for example as described in Danielson (Danielson M E, Beck T J, Karlamangla A S, Greendale G A, Atkinson E J, Lian Y, Khaled A S, Keaveny T M, Kopperdahl D, Ruppert K, Greenspan S, Vuga M, Cauley J A: A comparison of DXA and CT based methods for estimating the strength of the femoral neck in post-menopausal women. Osteoporos Int, 24:1379-88, 2013) and Pothuaud (Pothuaud L, Carceller P, Hans D: Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: applications in the study of human trabecular bone microarchitecture. Bone 42:775-87, 2008)—all of which are expressly included herein in their entireties by reference. X-ray images can also be analyzed for bone structure and strength, as described by U.S. patent application Ser. No. 11/855,939 and U.S. Pat. No. 8,073,521. Magnetic resonance scans, ultrasound scans, and CT scans can also be processed in various ways to provide structural measures associated with the strength and structure of the bone, for example, as described by Burghardt (High-resolution computed tomography for clinical imaging of bone microarchitecture. Burghardt A J, Link T M, Majumdar S. Clin Orthop Relat Res. 469:2179-93, 2011), Wehrli (Wehrli F W, Gomberg B R, Saha P K, Song H K, Hwang S N, Snyder P J: Digital topological analysis of in vivo magnetic resonance microimages of trabecular bone reveals structural implications of osteoporosis. J Bone Miner Res 16:1520-1531, 2001), Grimal (Quantitative ultrasound of cortical bone in the femoral neck predicts femur strength: results of a pilot study. Grimal Q, Grondin J, Guérard S, Barkmann R, Engelke K, Glüer C C, Laugier P. J Bone Miner Res. 28:302-12, 2013), Kazakia (In vivo determination of bone structure in postmenopausal women: a comparison of HR-pQCT and high-field MR imaging. Kazakia G J, Hyun B, Burghardt A J, Krug R, Newitt D C, de Papp A E, Link T M, Majumdar S. J Bone Miner Res. 23:463-74, 2008), and Bredbenner (Bredbenner T L, Mason R L, Havill L M, Orwoll S, Nicolella D P. Investigating fracture risk classifiers based on statistical shape and density modeling and the MrOS data set. In: Orthopaedic Research Society: Proceedings of the Annual Meeting of the Orthopaedic Research Society: 2012; San Francisco, Calif.)—all of which are expressly included herein in their entireties by reference.
When an interventional threshold value from any type of structural analysis of a bone is used clinically by a physician to decide on the course of action of medical management of a patient, any such interventional threshold value needs to be extensively validated and approved by a regulatory agency (e.g. the Food & Drug Administration in the U.S.). One approach to identifying values of an effective interventional threshold from a structural analysis of a bone is to conduct a series of fracture-outcome clinical studies in order to explore the ability of the new test to identify patients at high risk of fracture, and then on the basis of such studies choose a value of the interventional threshold. This is effectively a calibration process. Then, there is an additional need to prospectively validate this choice of threshold value in a general fashion, which typically requires multiple additional fracture-outcomes studies, independent of the clinical studies conducted in the calibration phase. Overall, this is a very expensive and time enduring developmental process since prospective fracture-outcome studies can take a long time to conduct (5-7 years) and oftentimes can involve many hundreds or thousands of patients. Thus, there is a need to facilitate this overall calibration and validation process so it is less expensive and time-consuming.
In addition, since clinical studies consistently show that compliance—the rates of patients taking their recommended medication—for osteoporosis drug treatment is low, there is also a need in developing any new clinical tests based on a structural analysis of a bone to help patients who are identified at high risk of fracture to better appreciate the extent of their compromised bone strength so that they are better motivated to undergo appropriate medical treatment. This lack of compliance is a major problem in treating osteoporosis since therapies need to be continued for multiple years for sustained protection. Typical medical reports generated from a DXA test consist of an X-ray type of picture of the patient's bone, numerical values of BMD and T-scores, and some charts of such values plotted against age, oftentimes with zones of “osteoporosis” highlighted on the chart. Also, certain “risk calculator” tools are also available that can report an absolute risk of fracture, such as a 5- or 10-year probability of fracture, expressed as a percentage. As a result of the difficulty in interpreting such medical reports and numerical scores, and the sometimes abstract and non-intuitive nature of the information in the report from a patient's perspective, and since osteoporosis is not painful unless there is a bone fracture, many patients who have not yet fractured but who are at high risk of a fracture find it difficult to really understand the outcome of the imaging tests or appreciate the severity of their medical condition. Thus, there is a need for a medical assessment/report that is more intuitive and easy to understand by the typical patient.